Maarten Breddels
where else to find them
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what they talk about
all 6 in the filters
where they keep going back
- PyData Global2 editions2022–2024
all 5 in the filters
on a stage with
who talks about the same things
- Jovan Veljanoski3 talks
- Dharhas Pothina5 talks
- Philipp Rudiger5 talks
- Matthew Rocklin13 talks
- Martin Durant8 talks
- Bryan Van de Ven4 talks
| title | conference | date |
|---|---|---|
| Python Apps in the Browser made simple by PyCafe | PyData Global 2024 | 2024-12-03 |
Claims a single-click zero-cost deploy for live Python apps and demonstrates it by running Streamlit Dash Panel Solara and (soon) Gradio entirely in the… | ||
| Keynote: Solara simplifies building complex dashboards. | PyData Eindhoven 2023 | 2023-11-30 |
Solara claims to let teams build complex data apps in 100% Python by combining a ReactJS‑style component API with reactive variables auto-trigger rerenders and… | ||
| Vaex: the perfect DataFrame Library for Python Apps | PyData Global 2022 | 2022-12-01 |
Vaex claims real interactivity on a single node for datasets larger than RAM — memory‑mapped columns, C++ kernels and lazy evaluation let it stream a 350 GB… | ||
| Uncluster Your Data Science Using Vaex | GOTO Copenhagen 2021 | 2021-11-08 |
Vaex is an out‑of‑core DataFrame that uses memory mapping, columnar storage and a lazy expression system to run aggregations on a single laptop instead of a… | ||
| A new approach to DataFrames and pipelines | PyData London 2019 | 2019-07-12 |
Vaex claims a working DataFrame that handles billion‑row tables on a laptop by memory‑mapping columnar storage (Apache Arrow / hdf5) and keeping an immutable… | ||
| A billion stars in the Jupyter Notebook | PyData Amsterdam 2017 | 2017-04-07 |
Vaex exposes columnar HDF5-backed tables and processes a billion rows per second by memory‑mapping and streaming chunked expressions to compute N‑dimensional… | ||
| nothing matches | ||